#!/usr/bin/env python3

from __future__ import print_function

import os

import cv2
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image

from ..parsing_model.model import BiSeNet


# Converts a Tensor into a Numpy array
# |imtype|: the desired type of the converted numpy array
def tensor2im(image_tensor, imtype=np.uint8, normalize=True):
    if isinstance(image_tensor, list):
        image_numpy = []
        for i in range(len(image_tensor)):
            image_numpy.append(tensor2im(image_tensor[i], imtype, normalize))
        return image_numpy
    image_numpy = image_tensor.cpu().float().numpy()
    if normalize:
        image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0
    else:
        image_numpy = np.transpose(image_numpy, (1, 2, 0)) * 255.0
    image_numpy = np.clip(image_numpy, 0, 255)
    if image_numpy.shape[2] == 1 or image_numpy.shape[2] > 3:
        image_numpy = image_numpy[:, :, 0]
    return image_numpy.astype(imtype)


# Converts a one-hot tensor into a colorful label map
def tensor2label(label_tensor, n_label, imtype=np.uint8):
    if n_label == 0:
        return tensor2im(label_tensor, imtype)
    label_tensor = label_tensor.cpu().float()
    if label_tensor.size()[0] > 1:
        label_tensor = label_tensor.max(0, keepdim=True)[1]
    label_tensor = Colorize(n_label)(label_tensor)
    label_numpy = np.transpose(label_tensor.numpy(), (1, 2, 0))
    return label_numpy.astype(imtype)


def save_image(image_numpy, image_path):
    image_pil = Image.fromarray(image_numpy)
    image_pil.save(image_path)


def mkdirs(paths):
    if isinstance(paths, list) and not isinstance(paths, str):
        for path in paths:
            mkdir(path)
    else:
        mkdir(paths)


def mkdir(path):
    if not os.path.exists(path):
        os.makedirs(path)


###############################################################################
# Code from
# https://github.com/ycszen/pytorch-seg/blob/master/transform.py
# Modified so it complies with the Citscape label map colors
###############################################################################
def uint82bin(n, count=8):
    """returns the binary of integer n, count refers to amount of bits"""
    return "".join([str((n >> y) & 1) for y in range(count - 1, -1, -1)])


def labelcolormap(N):
    if N == 35:  # cityscape
        cmap = np.array(
            [
                (0, 0, 0),
                (0, 0, 0),
                (0, 0, 0),
                (0, 0, 0),
                (0, 0, 0),
                (111, 74, 0),
                (81, 0, 81),
                (128, 64, 128),
                (244, 35, 232),
                (250, 170, 160),
                (230, 150, 140),
                (70, 70, 70),
                (102, 102, 156),
                (190, 153, 153),
                (180, 165, 180),
                (150, 100, 100),
                (150, 120, 90),
                (153, 153, 153),
                (153, 153, 153),
                (250, 170, 30),
                (220, 220, 0),
                (107, 142, 35),
                (152, 251, 152),
                (70, 130, 180),
                (220, 20, 60),
                (255, 0, 0),
                (0, 0, 142),
                (0, 0, 70),
                (0, 60, 100),
                (0, 0, 90),
                (0, 0, 110),
                (0, 80, 100),
                (0, 0, 230),
                (119, 11, 32),
                (0, 0, 142),
            ],
            dtype=np.uint8,
        )
    else:
        cmap = np.zeros((N, 3), dtype=np.uint8)
        for i in range(N):
            r, g, b = 0, 0, 0
            id = i
            for j in range(7):
                str_id = uint82bin(id)
                r = r ^ (np.uint8(str_id[-1]) << (7 - j))
                g = g ^ (np.uint8(str_id[-2]) << (7 - j))
                b = b ^ (np.uint8(str_id[-3]) << (7 - j))
                id = id >> 3
            cmap[i, 0] = r
            cmap[i, 1] = g
            cmap[i, 2] = b
    return cmap


def _totensor(array):
    tensor = torch.from_numpy(array)
    img = tensor.transpose(0, 1).transpose(0, 2).contiguous()
    return img.float().div(255)


def load_parsing_model(path, use_mask, use_gpu):
    if use_mask:
        n_classes = 19
        net = BiSeNet(n_classes=n_classes)
        if use_gpu:
            net.to("mps" if torch.backends.mps.is_available() else "cuda")
            net.load_state_dict(torch.load(path))
        else:
            net.cpu()
            net.load_state_dict(torch.load(path, map_location=torch.device("cpu")))

        net.eval()
        return net
    else:
        return None


def crop_align(
    detect_model, img_a_whole, crop_size, use_gpu, transformer_Arcface, swap_model
):

    face_detection = detect_model.get(img_a_whole, crop_size)
    if face_detection is None:
        return None

    img_a_align_crop, _ = face_detection
    img_a_align_crop_pil = Image.fromarray(
        cv2.cvtColor(img_a_align_crop[0], cv2.COLOR_BGR2RGB)
    )
    img_a = transformer_Arcface(img_a_align_crop_pil)
    img_id = img_a.view(-1, img_a.shape[0], img_a.shape[1], img_a.shape[2])

    # convert numpy to tensor
    img_id = (
        img_id.to("mps")
        if torch.backends.mps.is_available()
        else "cuda"
        if use_gpu
        else img_id.cpu()
    )

    # create latent id
    img_id_downsample = F.interpolate(img_id, size=(112, 112))
    id_vector = swap_model.netArc(img_id_downsample)
    id_vector = id_vector.detach().to("cpu")
    id_vector = id_vector / np.linalg.norm(id_vector, axis=1, keepdims=True)

    id_vector = id_vector.to("cuda") if use_gpu else id_vector.to("cpu")

    return id_vector


class Colorize(object):
    def __init__(self, n=35):
        self.cmap = labelcolormap(n)
        self.cmap = torch.from_numpy(self.cmap[:n])

    def __call__(self, gray_image):
        size = gray_image.size()
        color_image = torch.ByteTensor(3, size[1], size[2]).fill_(0)

        for label in range(0, len(self.cmap)):
            mask = (label == gray_image[0]).cpu()
            color_image[0][mask] = self.cmap[label][0]
            color_image[1][mask] = self.cmap[label][1]
            color_image[2][mask] = self.cmap[label][2]

        return color_image
